library(xcms)
## Loading required package: mzR
## Loading required package: Rcpp
## Loading required package: BiocGenerics
## Loading required package: parallel
##
## Attaching package: 'BiocGenerics'
## The following objects are masked from 'package:parallel':
##
## clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
## clusterExport, clusterMap, parApply, parCapply, parLapply,
## parLapplyLB, parRapply, parSapply, parSapplyLB
## The following objects are masked from 'package:stats':
##
## IQR, mad, xtabs
## The following objects are masked from 'package:base':
##
## anyDuplicated, append, as.data.frame, cbind, colnames,
## do.call, duplicated, eval, evalq, Filter, Find, get, grep,
## grepl, intersect, is.unsorted, lapply, lengths, Map, mapply,
## match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
## Position, rank, rbind, Reduce, rownames, sapply, setdiff,
## sort, table, tapply, union, unique, unsplit, which, which.max,
## which.min
## Loading required package: ProtGenerics
## Loading required package: Biobase
## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.
library(faahKO)
library(ggplot2)
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:xcms':
##
## groups
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
library(reshape2)
cdfpath <- system.file("cdf", package = "faahKO")
cdffiles <- list.files(cdfpath, recursive = TRUE, full.names = TRUE)
xr<-xcmsRaw(cdffiles[1])
xr
## An "xcmsRaw" object with 1278 mass spectra
##
## Time range: 2501.4-4499.8 seconds (41.7-75 minutes)
## Mass range: 200-600 m/z
## Intensity range: 70-1373180
##
## MSn data on 0 mass(es)
## with 0 MSn spectra
## Profile method: bin
## Profile step: 1 m/z (401 grid points from 200 to 600 m/z)
##
## Memory usage: 10.7 MB
plotTIC(xr)

xr@tic
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## [9] 969358 964664 971634 971365 974217 978026 970441 972351
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plot(xr@scantime,xr@tic)

plot(xr@scantime,xr@tic, type = 'l')

retention_time_ms<-xr@scantime
total_ion_chromatogram<-xr@tic
chrom <- data.frame(retention_time_ms,total_ion_chromatogram)
write.csv(chrom, 'chrom.csv', row.names = FALSE)
gg_chrom<-ggplot(chrom, aes(chrom$retention_time_ms)) +
geom_line(aes(y = chrom$total_ion_chromatogram))
gg_chrom

ggplotly(gg_chrom)
spec<-read.csv('chrom.csv',header = T)
gg_chrom<-ggplot(spec, aes(spec$retention_time_ms)) +
geom_line(aes(y = spec$total_ion_chromatogram))
library(dygraphs)
dygraph(spec) %>%
dyRangeSelector()